An asymptotically capacity-achieving scheme for the Gaussian multiple-access relay channel
Bibliographic record
Abstract
We study the multiple access relay channel (MARC) with relay-destination cooperation (MARC-RDC). This channel resembles the uplink transmission in heterogeneous network where two user equipments (UEs) communicate with macro-cell base station (BS) through small-cell BS. We propose a coding scheme where the transmission is carried over B blocks and each UE performs superposition block Markov encoding. The relay (small-cell BS) first jointly decodes both UEs information using sliding window decoding over two transmission blocks and then forwards these information to the destination (macro-cell BS) coherently with UEs. The destination quantizes its received signal in each block and forwards the quantization index to the relay. The destination then decodes both UEs information using backward decoding. For this scheme, we derive the achievable rate region and compare it with existing schemes and the cut-set bound. Results show that relay-destination cooperation enlarges the rate region as the destination power increases. We further show that the proposed scheme asymptotically achieves the capacity by reaching the cut-set bound when the destination power approaches infinity and the ratio of one UE-destination to UE-relay link amplitudes is equal to that of the other UE. These results make the proposed scheme appealing for deployment in 5G cellular networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".